RC1 The overall goal ofthe OAIC program is to increase scientific knowledge that will lead to effective ways to maintain or restore independence in older Americans. Innovative and appropriate analysis and data management methods are an important and necessary component to the scientific endeavor. A variety of designs, variables, hypotheses, and analyses are part ofthe Duke Pepper Center, all with the common theme of 'Pathways to Functional Decline'. Most of the studies assess the relationship of '-omic' markers to that pathway. An analysis core (AC) is proposed with 2 goals: (1) to provide an appropriate data management and analytic resources to the faculty, pilots, and projects in the Pepper Center, and (2) to develop innovative biostatistical analytic methodologies. The AC core is built to provide analytic support the junior and senior faculty across the range of designs and analytic issues inherent in the studies, including sociologists (latent variables), biostatisticians (design, longitudinal analysis, psychometrics), bioinformaticists (genetic and high dimensional data analysis), and statisticians for day-to-day monitoring of studies and data management. Data management will use secure web-based methods (REDCap), and methods from the Center on Human Genetics appropriate for managing high dimensional metabolomic, proteiomic, and genetic data. The panel of studies is constructed and managed so that standardized analytic methods and common measures across studies can be employed. Following our previous successes, the studies and methods lend themselves for use of meta-analytic techniques, allowing discovery of relationships, not possible in any small single study. In addition to provision of technical analytic and data management support, the core will provide consultation and training support to the faculty of the Pepper Center. The core will also pursue methodologic goals of interest to biostatisticians which address analytic issues encountered. In particular, in order to develop valid and reproducible models ofthe relationship between biomarkers and function, several analytic considerations must be developed, including Type-l error control for multiple testing, data aggregation, and measurement of change over time in several domains simultaneously. Working closely with the Biomarkers Cores (RC2 and RC3), we will focus on methods for examining trajectories of change in the biological and clinical variables, establish temporal ordering, assess mediation and moderation pathways, assess the constancy of the relationships across studies, and develop appropriate methods for complex error structures which result from complex sampling designs.